Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches
The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squee...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/293976 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832549842068439040 |
|---|---|
| author | Manjunath Patel Gowdru Chandrashekarappa Prasad Krishna Mahesh B. Parappagoudar |
| author_facet | Manjunath Patel Gowdru Chandrashekarappa Prasad Krishna Mahesh B. Parappagoudar |
| author_sort | Manjunath Patel Gowdru Chandrashekarappa |
| collection | DOAJ |
| description | The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs. |
| format | Article |
| id | doaj-art-24df124801b44fda8a9c61d41ab74f35 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-24df124801b44fda8a9c61d41ab74f352025-02-03T06:08:20ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322014-01-01201410.1155/2014/293976293976Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based ApproachesManjunath Patel Gowdru Chandrashekarappa0Prasad Krishna1Mahesh B. Parappagoudar2Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, IndiaDepartment of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, IndiaDepartment of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh 491001, IndiaThe present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.http://dx.doi.org/10.1155/2014/293976 |
| spellingShingle | Manjunath Patel Gowdru Chandrashekarappa Prasad Krishna Mahesh B. Parappagoudar Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches Applied Computational Intelligence and Soft Computing |
| title | Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches |
| title_full | Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches |
| title_fullStr | Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches |
| title_full_unstemmed | Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches |
| title_short | Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches |
| title_sort | forward and reverse process models for the squeeze casting process using neural network based approaches |
| url | http://dx.doi.org/10.1155/2014/293976 |
| work_keys_str_mv | AT manjunathpatelgowdruchandrashekarappa forwardandreverseprocessmodelsforthesqueezecastingprocessusingneuralnetworkbasedapproaches AT prasadkrishna forwardandreverseprocessmodelsforthesqueezecastingprocessusingneuralnetworkbasedapproaches AT maheshbparappagoudar forwardandreverseprocessmodelsforthesqueezecastingprocessusingneuralnetworkbasedapproaches |